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Data Science Paper
##Lillian Heitz and Ainsley Snell ##Introduction:
Earlier this year, uncontrollable wildfires in the Los Angeles area destroyed over 18,000 homes and structures leaving 200,000 people to evacuate the region. Along with property damage, the wildfires caused a significant decline in air quality leading to a health emergency in LA county. The financial costs of just the Palidasas and Eaton wildfires could reach over $150 billion with insured losses along covering $8 billion. Our project is inspired by these California Wildfires and their disastrous aftermath that have left California in a vulnerable state. We aim to predict current wildfire risk across California’s 58 counties to draw correlation as to why certain counties are more at risk than others. We plan to do this using past fire data, including 2020 wildfire perimeters, alongside historical weather data taken from the California National Resource Agency (CNRA). The weather data consists of average monthly temperature and precipitation. Despite the influence for the project being the 2025 Palisades fires, there is not enough current data to determine their specific correlation. We chose to analyse the year 2020, being the most current year with the most data. We hypothesize that higher temperature and less rain would correlate with areas with more fires, making dryer and higher temperature areas more susceptible to wildfires.